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Energy Supply Management Using the Solar Energy Prediction in Smart Grid

Abdelilah Kahaji(1*), Rachid Alaoui(2), Sadik Farhat(3), Lahoussine Bouhouch(4)

(1) Faculty of Science, Ibn Zohr University, Morocco
(2) Faculty of Science, Ibn Zohr University, Morocco
(3) Faculty of Science, Ibn Zohr University, Morocco
(4) Faculty of Science, Ibn Zohr University, Morocco
(*) Corresponding author


DOI: https://doi.org/10.15866/iree.v11i5.9593

Abstract


The most significant challenge in a smart grid (SG) is the control of the function of supply and the demand of electric energy. Indeed, some difficulties arise during the management of the electric energy storage and the intermittency of renewable energy. In particular, these problems are encountered during the preparations of the product promotions and the sales price updates for one to several days (j + n). The prediction of these types of energy is only possible through smart tools and solutions.This paper therefore presents a work proponing the development of an optimized method for a daily solar energy prediction. This modelisbased on the neural network multilayer perceptron model (MLP) and on the preprocessing techniques stationarity of time series (TS) obtained by the Autoregressive moving average method (ARMA). This method consists in making the historical measures of the solar irradiation stationary before implementing the MLP model. The incorporation of this technique has helped to reduce the root Mean Square Error (RMSE) to 0.51 kWh/m2/day, hence obtaininga reduction of 0.11 kWh/m2/day of the RMSE (-16%) compared to the MLP model without preprocessing of the stationarity developed in advance. This optimization has significantly improved the prediction of the daily solar irradiation. It has set the present MLP model among the best predictors studied in the many recent researches in the literature.
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Keywords


Smart Grid; Prediction; Energy Supply Management; Multilayer Perceptron Model; Renewable Energy; Solar Energy

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References


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